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William KleiberView profile

Associate Professor

• Spatial Statistics
• Geostatistics
• Uncertainty Quantification
• +5 more

William Kleiber is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder, specializing in spatial statistics for geophysical applications and renewable energy systems. His work bridges advanced statistical theory with practical environmental challenges, particularly in climate modeling and uncertainty quantification. Education: Ph.D. in Statistics, University of Washington (2010) Dr. Kleiber's research centers on spatial statistics and geostatistics for large-scale nonstationary datasets, with significant contributions to statistical climatology , stochastic weather generators , and uncertainty quantification in geophysical models. His methodologies address critical energy science challenges, including solar irradiance forecasting and renewable resource assessment. Recent work integrates machine learning with spatial statistics to handle massive multivariate environmental data, advancing predictive capabilities for climate extremes and renewable energy systems. His 2023-2025 publications reveal a strong trend toward hybrid statistical-machine learning approaches for environmental data, emphasizing uncertainty-aware predictions in snow hydrology, sea surface temperature reconstruction, and extreme weather events. This work consistently tackles the computational and theoretical challenges of high-dimensional spatial processes. Scientific Awards: American Statistical Association's Section on Statistics and the Environment 2016 Young Investigator Award Dr. Kleiber has served on the Board of Directors for The International Environmetrics Society and the American Statistical Association's Section on Statistics and the Environment, and contributed to editorial boards of leading statistical journals. His research has been supported by collaborative grants including 'Collaborative Research: Theory and Methods for Highly Multivariate Spatial Processes with Applications to Climate Data Science' (2018) and 'Scalable Statistical Validation for Large Spatio-Temporal Datasets' (2014). Current projects focus on integrating deep learning with spatial statistics for renewable energy forecasting and climate risk assessment. He maintains active international collaborations, evidenced by his 2016 Lebesgue Chair visiting professorship at University of Rennes 1, France, and partnerships with the National Center for Atmospheric Research where he previously conducted postdoctoral research.

Eli Ben-MichaelView profile

Assistant Professor

• Causal Inference
• Machine Learning
• Public Policy
• +8 more

Eli Ben-Michael is an Assistant Professor jointly appointed in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a postdoctoral fellow at Harvard University and earned his PhD in Statistics from UC Berkeley. Education: PhD in Statistics, UC Berkeley (2016–2020) Bachelor’s in Computer Science & Statistics, Columbia University Research Interests: Dr. Ben-Michael focuses on developing statistical and computational methods for causal inference and data-driven policy evaluation. His work bridges statistics, machine learning, and optimization to address challenges in public policy, healthcare equity, and social science. Key areas include synthetic control methods, balancing weights, safe policy learning, and causal effects in natural language processing. His research has been applied to topics such as gun control policies, healthcare disparities, and pre-trial risk assessment. Key Contributions: His methodological innovations include the Augmented Synthetic Control method and tools for policy learning under uncertainty. He has developed open-source R packages like augsynth and balancer to implement his methods. Labs/Teams: He is part of the AIMSEC collaboration, advancing AI measurement science, and contributes to interdisciplinary teams studying policy impacts in healthcare, education, and criminal justice.

Marina RiabizView profile

Lecturer

• Computational Statistics
• Probabilistic Machine Learning
• Bayesian Inference
• +3 more

Dr. Marina Riabiz is a Lecturer in Statistics at King's College London's Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. She holds a PhD from the University of Cambridge and a Master’s in Mathematical Engineering from Politecnico di Milano. Her research focuses on computational statistics, probabilistic machine learning, Bayesian inference, and medical engineering applications. She co-leads the DT4Health Centre for Doctoral Training, which develops digital twins for healthcare innovation. Key projects include uncertainty quantification in cardiac models and virtual patient cohort simulations. Her work spans MCMC optimization, Gaussian approximation, and state space models with stable processes. She has contributed to high-impact journals and conferences, including Annual Review of Statistics and IEEE Transactions. Education: PhD in Signal Processing, University of Cambridge (UK) MSc in Mathematical Engineering, Politecnico di Milano (Italy) BSc in Mathematical Engineering, Politecnico di Milano (Italy) Research Interests: Bayesian computational methods Medical data science Uncertainty quantification Machine learning integration with statistical inference Publications Overview: Riabiz’s recent work emphasizes MCMC postprocessing, optimal thinning algorithms, and Gaussian approximation for stable noise systems. Her cardiac modelling research bridges computational statistics with biomedical applications, including electrophysiology model calibration and virtual patient cohorts. These contributions advance both theoretical statistics and translational healthcare technologies. Awards: No specific awards listed. Grants and Collaborations: Active in multidisciplinary teams at King’s College London and the Alan Turing Institute. Leads DT4Health’s recruitment and mentoring initiatives. Labs/Teams: Cardiac Electro-Mechanics Research Group (BMEIS), Centre for Doctoral Training in DT4Health.

Wen YeView profile

Assistant Professor

• User Experience Design
• Empathy-Driven Design
• Wearable Technology
• +3 more

Wen (Vivian) Ye is an Assistant Professor of Practice in the Department of Design at the University of Texas at Austin. She holds dual Master of Fine Arts degrees from the University of California–Davis and the Marangoni Institute in Italy. Her academic role combines teaching design thinking and advanced design studio courses with industry experience from roles as a fashion designer at VERO MODA and a global fashion buyer for Armani. Her research focuses on user experience, empathy-driven design, and wearable technology, emphasizing cross-disciplinary collaboration. Ye actively participates in National Science Foundation (NSF) projects aimed at improving transportation and power systems through electric vehicles and clean energy solutions. She designs educational materials for these initiatives, bridging academic and industrial applications. Her work highlights innovation in sustainable technology and human-centered design principles. Her recent publications span data science code generation, neural network adaptation, and uncertainty quantification in machine learning. These contributions address foundational challenges in AI and computational methodologies, reflecting her dual expertise in design and technical systems.

Daniel GöhringView profile

Professor

• Autonomous Vehicles
• Mobile Robotics
• Computer Vision
• +6 more

Prof. Daniel Göhring is a professor in the Department of Computer Science at the Free University of Berlin, leading the Autonomous Cars Lab and part of the Dahlem Center for Machine Learning and Robotics. His research emphasizes robotic perception, object tracking, and real-time planning under computational constraints, with a focus on autonomous vehicles and cooperative systems. Education: Bachelor's/Master's in Robotics (exact program unspecified) PhD in Computer Science at Humboldt University Berlin Postdoctoral Research at International Computer Science Institute (ICSI), Berkeley, CA Research Interests: Daniel's work integrates machine learning and sensor technologies like LiDAR and cameras to address challenges in autonomous driving. Key areas include SLAM algorithms, trajectory prediction, cooperative perception, and real-time systems. He explores how limited sensor data and computational resources can be optimized for dynamic traffic environments. Grants and Projects: Leader of the Autonomous Cars Lab Involved in EU-funded projects such as H2020 HIVEOPOLIS and KIS-M (AI-based mobility systems) Past projects include CRTX (recycling optimization), Open.Make (open hardware), RoboFish (biological swarm analysis), and SAFARI Awards: Best Poster Award at IAAS Workshop 2024 Best Paper Award at ICAIR-CACRE 2019 Teaching: He has taught courses such as Image Processing, Robotics, and Advanced Robotics. Recent semesters include modules on self-supervised learning, autonomous vehicle research, and continuous learning software projects. Labs and Teams: Daniel heads the Autonomous Cars Lab and collaborates with the BioRobotics Lab, focusing on interdisciplinary projects like 'Robots Communicating with Fish' and 'Open Hardware for FAIR Robotics.'

• Signal Processing
• Communications Engineering
• Radar Systems
• +5 more

Assoc. Professor Qinghua Guo holds a position at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering. He earned his PhD from City University of Hong Kong (2008) and has been affiliated with the University of Wollongong since 2019. His research focuses on signal processing, communications, radar systems, machine learning, and optical sensing, with notable contributions to graphical models and Bayesian inference. Prof. Guo is recognized as a top 2% influential scientist globally (Stanford University) and a leader in Electronics and Computer Science (Research.com). He serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters, among other roles. His teaching spans undergraduate to postgraduate levels, including specialized subjects in engineering and information sciences. His recent research emphasizes integrated sensing and communication systems, federated learning, and secure beamforming in 5G/6G networks. He has supervised over 30 PhD and Master’s students in areas like MIMO systems, signal processing for IoT, and quantum sensing. Key funded projects include AFDM-SCMA (2025–2028) for IoT connectivity and Grant-Free Multiple Access for LEO satellite networks (2023–2027). His work bridges theoretical advancements with practical applications in robotics, energy systems, and defense technologies.

• 4D Seismic Analysis
• Reservoir Engineering
• Carbon Capture & Storage
• +3 more

Gustavo Corte is a Research Associate at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, affiliated with the Institute for GeoEnergy Engineering. His research focuses on 4D seismic inversion, reservoir engineering, and carbon capture applications. He has contributed to projects involving Bayesian inversion techniques, machine learning applications in seismic data analysis, and CO2 dissolution modeling in North Sea reservoirs. Research interests include dynamic reservoir monitoring through seismic data, probabilistic estimation of subsurface parameters, and integration of geophysical data with reservoir simulation tools. His work emphasizes practical applications in hydrocarbon fields and carbon storage validation. Key collaborations include studies on the Catcher Fields and West of Shetlands regions. Corte's publications span peer-reviewed journals like Geophysical Prospecting and conference proceedings, with a focus on advancing seismic data interpretation methods. His work has been cited 36 times and accessed widely in academic platforms.

Xuefei LUView profile

Associate Professor

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +9 more

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

Thierry KleinView profile

Professor

• Probability Theory
• Statistics
• Uncertainty Quantification
• +4 more

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.

Barbaros YetView profile

Associate Professor

• Decision-Making Under Uncertainty
• Bayesian Approaches
• Causal and Probabilistic Graphical Models
• +5 more

Barbaros Yet is an Associate Professor in the Department of Cognitive Science at the Graduate School of Informatics, Middle East Technical University in Ankara, Turkey. His research focuses on decision-making under uncertainty, integrating human judgment, published evidence, and uncertain data into Bayesian approaches and probabilistic graphical models. Applications span healthcare (clinical decision support, trauma care modeling), agriculture (climate resilience policy), and operational systems (fleet reliability prediction). Research interests include Bayesian networks, causal reasoning, knowledge representation, and decision support systems. He has developed tools for shared clinical decision-making, trauma outcome prediction, and agricultural policy prioritization. His work emphasizes interpretable models that align with expert reasoning processes. Notable contributions include Bayesian network-based frameworks for spinal pathology assessment, aircraft reliability modeling, and trauma care prediction systems. His articles frequently bridge theoretical probabilistic modeling with practical applications in medicine and engineering. Active in interdisciplinary collaborations, he contributes to global health initiatives like the UKRI Action Against Stunting Hub. Professional links include ORCID (0000-0003-4058-2677), ResearchGate (https://www.researchgate.net/profile/Barbaros-Yet), and a Twitter presence (@barbarosyet). Contact via byet@metu.edu.tr.

Zenon Medina-CetinaView profile

Associate Professor

• Stochastic Geomechanics
• Risk and Reliability Analysis
• Bayesian Networks
• +7 more

Zenon Medina-Cetina is an Associate Professor in the Zachry Department of Civil & Environmental Engineering at Texas A&M University (TAMU), with affiliations to the Mosbacher Institute for Trade, Economics, and Public Policy at the Bush School of Government and Public Service. He holds courtesy joint appointments in Petroleum Engineering, Ocean Engineering, Multidisciplinary Engineering, and Geography (College of Arts and Sciences). Dr. Medina-Cetina specializes in probabilistic risk assessment of geo-systems, stochastic geomechanics, and integrated site characterization. His work bridges engineering, geoscience, and computational methods to address complex challenges in infrastructure resilience, offshore geotechnics, and environmental systems. Education: Ph.D. (2006), M.Sc. (2005) in Civil Engineering from Johns Hopkins University; M.E. (1996) in Geostatistics from Universidad Nacional Autónoma de México (UNAM); B.Sc. (1994) in Soil Dynamics from Universidad Autónoma de Yucatán (UADY). He completed postdoctoral research at SIMULA and the Norwegian Geotechnical Institute (NGI). Research Interests: Stochastic processes and spatio-temporal simulation (3D-T) Risk, reliability, and sensitivity analysis Uncertainty quantification and inverse problems Bayesian networks and machine learning applications Optimal modeling of geomechanical systems Arctic and Gulf of Mexico geohazard mitigation Awards & Recognition: 2024: Mosbacher Research Fellow, College of Engineering Excellence Award 2022: Zachry Career Development Professorship II, Excellence in Teaching Award 2012: Fellow, Society for Underwater Technology 2001: Fulbright Scholarship Grants & Projects: Lead investigator of $7.4M+ in research funding from federal agencies (e.g., DHS, NSF) and industry Director of the Stochastic Geomechanics Laboratory (SGL) Principal on initiatives like the Arctic X-Grant and Yucatán Initiative Co-lead of the Network-to-Network for the Gulf of Mexico Teaching & Mentoring: Developed TAMU’s Introduction to Research Abroad Program (IRAP) Advised 14 doctoral and 13 master’s students, plus postdoctoral researchers Course instructor in Bayesian Risk Networks, Stochastic Mechanics, and Geomechanics Labs & Teams: Stochastic Geomechanics Laboratory (SGL): Focuses on data-driven risk modeling and computational geomechanics International collaborations with institutions in Mexico, Norway, and the Arctic region

Michael ColaresiView profile

Professor

• International Relations
• Political Methodology
• Computational Social Science
• +3 more

Michael Colaresi is the Associate Vice Provost for Data Science and William S. Dietrich II Professor of Political Science at the University of Pittsburgh, affiliated with the Dietrich School of Arts and Sciences. He holds roles in the Center for Research Computing and Data, Computational Social Science program, and the Intelligent Systems Program at the School of Computing and Information. His research focuses on computational social science, leveraging machine learning and text analysis to study national security secrecy, political violence, and human rights dynamics. Colaresi co-directs the Responsible Data Science, Complexity, and AI for Peace (RedSCAIP) Lab, emphasizing interdisciplinary collaboration to address societal challenges. Education: PhD in Political Science from Indiana University (2002). He founded the Social Science Data Analytics initiative at Michigan State University and now leads efforts in Pitt’s Institute for Cyber Law, Policy, and Security. His work has been funded by NSF grants and foundations like Hillman and RK Mellon. Research Interests: International Relations, computational methods, conflict forecasting, and the intersection of technology with democratic governance. His 2014 book *Democracy Declassified* explores secrecy dilemmas in liberal democracies and was shortlisted for the Conflict Research Society Prize. Recent projects include the ViEWS political violence early-warning system and HR-VEAW human rights analytics platform. Awards: Co-winner of the 2017 Journal of Peace Research Best Visualization Award and 2006 Gosnell Prize for Political Methodology. He serves as co-editor of *International Interactions* and collaborates with institutions like the Peace Research Institute Oslo (PRIO) and Uppsala University’s Violence Early Warning Project. Teaching: Courses include Computational Social Science, Bayesian Statistics, and Text-as-Data methods. His pedagogical focus emphasizes bridging social science theory with modern computational tools.

• Porous Media
• Computational Mechanics
• Bayesian Inference
• +3 more

Mina Karimi is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at California Institute of Technology (Caltech). She is part of the Bhattacharya group, advised by Professor Kaushik Bhattacharya. Her research focuses on computational mechanics, poromechanics, and Bayesian inference applied to porous media systems. Key areas include reactive flow modeling, multiscale simulations, and data-driven approaches for subsurface engineering challenges. Her work integrates advanced computational methods with geomechanical and materials science problems, emphasizing energy systems and environmental applications. Recent studies explore carbon sequestration mechanisms, chemo-poro-mechanical coupling, and high-dimensional parameter estimation using Bayesian frameworks. She also investigates crack-healing phenomena in shape memory alloy composites and develops accelerated micromechanical models for solute transport. Publications span topics from machine learning-enhanced groundwater modeling to Hessian-informed sampling techniques for high-dimensional inverse problems. Her research bridges theoretical developments with practical applications in subsurface energy storage, geological carbon sequestration, and material behavior under extreme conditions. Advising is conducted under the mentorship of Prof. Bhattacharya, with affiliations to the Resnick Sustainability Institute at Caltech. Current efforts emphasize computational tools for poromechanics and uncertainty quantification in complex multiphase systems.

Hoshin V. GuptaView profile

Professor

• Surface Water Hydrology
• Climate Modeling
• Hydrologic Interactions with Climate
• +9 more

Hoshin V. Gupta is a Regents' Professor at the University of Arizona, holding joint appointments in the Department of Hydrology and Atmospheric Sciences, Technology-Public Policy/Markets, Global Change GIDP, and Remote Sensing and Spatial Analysis GIDP. He specializes in interdisciplinary research at the intersection of hydrology, climate science, and decision-making frameworks. Education: PhD in Systems Engineering from Case Western Reserve University (1984). Research focuses on surface water hydrology, climate modeling, and the integration of hydrologic processes with policy analysis. Techniques include computational modeling, machine learning, remote sensing data assimilation, and Bayesian methods. His work addresses uncertainty quantification, decision support systems, and the human-water nexus. Professional roles include membership in the Promotion and Tenure Committee and leadership in interdisciplinary initiatives through Global Change and Remote Sensing GIDPs.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +4 more

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.